Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7ff095ab9390>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7ff0959e1f60>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels) , name = 'input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name = 'input_z')
    learning_rate = tf.placeholder(tf.float32, [], name = 'learning_rate')
    
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Leaky ReLU

In [6]:
def leaky_relu(x, alpha=0.05, name='leaky_relu'): 
    return tf.maximum(alpha * x, x, name=name)

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [14]:
def discriminator(images, reuse=False, alpha = 0.2, rate = 0.1):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse = reuse):
        #input 28x28x3        
        images = tf.layers.conv2d(images, 128, 3, strides = 2, padding = 'same', kernel_initializer = tf.contrib.layers.xavier_initializer())                
        images = leaky_relu(images, alpha = alpha)
        images = tf.layers.dropout(images, rate, training= True)
        # 14x14x128
        
        images = tf.layers.conv2d(images, 256, 3, strides = 2, padding = 'same', use_bias=False, kernel_initializer = tf.contrib.layers.xavier_initializer())
        images = tf.layers.batch_normalization(images, training = True)
        images = leaky_relu(images, alpha = alpha)
        images = tf.layers.dropout(images, rate, training= True)
        #7x7x256
        
        images = tf.layers.conv2d(images, 512, 3, strides = 2, padding = 'same', use_bias=False, kernel_initializer = tf.contrib.layers.xavier_initializer())
        images = tf.layers.batch_normalization(images, training = True)
        images = leaky_relu(images, alpha = alpha)
        images = tf.layers.dropout(images, rate, training= True)
        #4x4x512
        
        #flatted
        flat = tf.reshape(images, (-1, 4 * 4 * 512))
        logits = tf.layers.dense(flat, 1, )
        out = tf.sigmoid(logits)
    
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2, rate = 0.1):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse = not is_train):
        x = tf.layers.dense(z, 7 * 7 * 512,  use_bias=False, kernel_initializer = tf.contrib.layers.xavier_initializer())
        x = tf.reshape(x, (-1, 7, 7, 512))
        x = tf.layers.batch_normalization(x, training = is_train)
        x = leaky_relu(x, alpha = alpha)   
        x = tf.layers.dropout(x, rate, training= is_train)
        # 7x7x512
        
        x = tf.layers.conv2d_transpose(x, 256, 5, strides = 1, padding = 'same', use_bias=False, kernel_initializer = tf.contrib.layers.xavier_initializer())       
        x = tf.layers.batch_normalization(x, training = is_train)
        x = leaky_relu(x, alpha = alpha)
        x = tf.layers.dropout(x, rate, training= is_train)
        # 7x7x256
        
        x = tf.layers.conv2d_transpose(x, 128, 5, strides = 2, padding = 'same', use_bias=False, kernel_initializer = tf.contrib.layers.xavier_initializer())       
        x = tf.layers.batch_normalization(x, training = is_train)
        x = leaky_relu(x, alpha = alpha)
        x = tf.layers.dropout(x, rate, training= is_train)
        # 14x14x128
        
        logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides = 2, padding = 'same', kernel_initializer = tf.contrib.layers.xavier_initializer())
        # 28, 28, out_channel_dim
        
        out = tf.tanh(logits)
    
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim, smooth = 0.1):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse = False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse = True)
        

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.random_uniform(tf.shape(d_model_real), 0.7, 1.2, tf.float32)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.random_uniform(tf.shape(d_logits_fake), 0, 0.3, tf.float32)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))


    d_loss = d_loss_real + d_loss_fake
    
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, 
          print_every=10, show_every=50):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
        
    # TODO: Build Model      
    
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):            
            for batch_images in get_batches(batch_size):
                # TODO: Train Model                
                steps = steps + 1
                images = batch_images * 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: images, input_z: batch_z, lr: learning_rate})
                                
                if steps % print_every == 0:
                   
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:                    
                    show_generator_output(sess, n_images = 16, 
                                          input_z = input_z, out_channel_dim = data_shape[3], image_mode = data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [15]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
tf.reset_default_graph()

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.9048... Generator Loss: 1.4293
Epoch 1/2... Discriminator Loss: 0.8907... Generator Loss: 1.4096
Epoch 1/2... Discriminator Loss: 0.9661... Generator Loss: 1.2903
Epoch 1/2... Discriminator Loss: 0.8737... Generator Loss: 2.2724
Epoch 1/2... Discriminator Loss: 1.3895... Generator Loss: 0.5449
Epoch 1/2... Discriminator Loss: 1.3545... Generator Loss: 0.7373
Epoch 1/2... Discriminator Loss: 1.3025... Generator Loss: 1.0606
Epoch 1/2... Discriminator Loss: 1.2244... Generator Loss: 1.2276
Epoch 1/2... Discriminator Loss: 1.1810... Generator Loss: 0.7516
Epoch 1/2... Discriminator Loss: 1.0227... Generator Loss: 0.9137
Epoch 1/2... Discriminator Loss: 1.0823... Generator Loss: 0.7456
Epoch 1/2... Discriminator Loss: 1.4213... Generator Loss: 0.5380
Epoch 1/2... Discriminator Loss: 1.3125... Generator Loss: 0.6887
Epoch 1/2... Discriminator Loss: 1.4333... Generator Loss: 0.5186
Epoch 1/2... Discriminator Loss: 1.3121... Generator Loss: 0.9300
Epoch 1/2... Discriminator Loss: 1.0665... Generator Loss: 1.1984
Epoch 1/2... Discriminator Loss: 1.1928... Generator Loss: 0.6386
Epoch 1/2... Discriminator Loss: 1.3488... Generator Loss: 0.5662
Epoch 1/2... Discriminator Loss: 1.1950... Generator Loss: 1.0890
Epoch 1/2... Discriminator Loss: 1.1108... Generator Loss: 0.8386
Epoch 1/2... Discriminator Loss: 1.1359... Generator Loss: 1.2843
Epoch 1/2... Discriminator Loss: 1.1298... Generator Loss: 0.6961
Epoch 1/2... Discriminator Loss: 1.1935... Generator Loss: 1.2538
Epoch 1/2... Discriminator Loss: 1.0792... Generator Loss: 1.0816
Epoch 1/2... Discriminator Loss: 1.3770... Generator Loss: 0.4272
Epoch 1/2... Discriminator Loss: 1.1493... Generator Loss: 0.7355
Epoch 1/2... Discriminator Loss: 1.2979... Generator Loss: 0.5513
Epoch 1/2... Discriminator Loss: 1.1884... Generator Loss: 0.8228
Epoch 1/2... Discriminator Loss: 1.0579... Generator Loss: 0.9115
Epoch 1/2... Discriminator Loss: 1.0622... Generator Loss: 0.8652
Epoch 1/2... Discriminator Loss: 1.1535... Generator Loss: 0.8364
Epoch 1/2... Discriminator Loss: 1.0133... Generator Loss: 1.0906
Epoch 1/2... Discriminator Loss: 1.1366... Generator Loss: 1.0899
Epoch 1/2... Discriminator Loss: 1.0708... Generator Loss: 0.8973
Epoch 1/2... Discriminator Loss: 1.0065... Generator Loss: 0.8837
Epoch 1/2... Discriminator Loss: 1.1428... Generator Loss: 0.7979
Epoch 1/2... Discriminator Loss: 1.1542... Generator Loss: 0.6537
Epoch 1/2... Discriminator Loss: 1.2623... Generator Loss: 1.6560
Epoch 1/2... Discriminator Loss: 1.1038... Generator Loss: 0.6788
Epoch 1/2... Discriminator Loss: 1.1159... Generator Loss: 0.6345
Epoch 1/2... Discriminator Loss: 1.0762... Generator Loss: 0.7516
Epoch 1/2... Discriminator Loss: 0.9569... Generator Loss: 0.9102
Epoch 1/2... Discriminator Loss: 1.2007... Generator Loss: 1.5063
Epoch 1/2... Discriminator Loss: 1.1720... Generator Loss: 0.7625
Epoch 1/2... Discriminator Loss: 1.2075... Generator Loss: 0.6122
Epoch 1/2... Discriminator Loss: 1.1548... Generator Loss: 1.5305
Epoch 1/2... Discriminator Loss: 1.0361... Generator Loss: 1.0775
Epoch 1/2... Discriminator Loss: 1.3118... Generator Loss: 0.4751
Epoch 1/2... Discriminator Loss: 1.1562... Generator Loss: 0.5281
Epoch 1/2... Discriminator Loss: 1.2852... Generator Loss: 0.5664
Epoch 1/2... Discriminator Loss: 1.0761... Generator Loss: 0.7991
Epoch 1/2... Discriminator Loss: 1.2214... Generator Loss: 1.3511
Epoch 1/2... Discriminator Loss: 1.2670... Generator Loss: 0.6376
Epoch 1/2... Discriminator Loss: 1.0758... Generator Loss: 0.9386
Epoch 1/2... Discriminator Loss: 1.1207... Generator Loss: 0.8247
Epoch 1/2... Discriminator Loss: 1.5221... Generator Loss: 0.3273
Epoch 1/2... Discriminator Loss: 1.3538... Generator Loss: 1.4957
Epoch 1/2... Discriminator Loss: 1.1645... Generator Loss: 0.7111
Epoch 1/2... Discriminator Loss: 1.2076... Generator Loss: 0.6993
Epoch 1/2... Discriminator Loss: 1.2903... Generator Loss: 0.4304
Epoch 1/2... Discriminator Loss: 1.1953... Generator Loss: 0.5550
Epoch 1/2... Discriminator Loss: 1.0967... Generator Loss: 0.7573
Epoch 1/2... Discriminator Loss: 1.0427... Generator Loss: 0.9770
Epoch 1/2... Discriminator Loss: 1.2397... Generator Loss: 1.4463
Epoch 1/2... Discriminator Loss: 1.2439... Generator Loss: 0.6356
Epoch 1/2... Discriminator Loss: 1.0935... Generator Loss: 0.8429
Epoch 1/2... Discriminator Loss: 1.2159... Generator Loss: 1.1933
Epoch 1/2... Discriminator Loss: 1.1863... Generator Loss: 0.5959
Epoch 1/2... Discriminator Loss: 1.0391... Generator Loss: 1.0152
Epoch 1/2... Discriminator Loss: 1.2088... Generator Loss: 0.6721
Epoch 1/2... Discriminator Loss: 1.0408... Generator Loss: 0.8599
Epoch 1/2... Discriminator Loss: 1.1338... Generator Loss: 0.6632
Epoch 1/2... Discriminator Loss: 1.0635... Generator Loss: 0.8363
Epoch 1/2... Discriminator Loss: 1.0335... Generator Loss: 0.7634
Epoch 1/2... Discriminator Loss: 1.2062... Generator Loss: 0.5410
Epoch 1/2... Discriminator Loss: 1.2723... Generator Loss: 0.4745
Epoch 1/2... Discriminator Loss: 1.0421... Generator Loss: 1.1927
Epoch 1/2... Discriminator Loss: 1.1191... Generator Loss: 0.9377
Epoch 1/2... Discriminator Loss: 1.3693... Generator Loss: 1.3653
Epoch 1/2... Discriminator Loss: 1.0717... Generator Loss: 0.9209
Epoch 1/2... Discriminator Loss: 1.2541... Generator Loss: 0.5345
Epoch 1/2... Discriminator Loss: 1.1435... Generator Loss: 1.2665
Epoch 1/2... Discriminator Loss: 1.1084... Generator Loss: 0.8237
Epoch 1/2... Discriminator Loss: 1.1535... Generator Loss: 0.9413
Epoch 1/2... Discriminator Loss: 1.1302... Generator Loss: 0.7616
Epoch 1/2... Discriminator Loss: 1.1264... Generator Loss: 0.8181
Epoch 1/2... Discriminator Loss: 1.2276... Generator Loss: 0.4445
Epoch 1/2... Discriminator Loss: 1.1349... Generator Loss: 0.6689
Epoch 1/2... Discriminator Loss: 1.1409... Generator Loss: 1.0415
Epoch 1/2... Discriminator Loss: 1.0861... Generator Loss: 1.2023
Epoch 1/2... Discriminator Loss: 1.3570... Generator Loss: 0.5335
Epoch 1/2... Discriminator Loss: 1.0050... Generator Loss: 0.9507
Epoch 1/2... Discriminator Loss: 1.0110... Generator Loss: 1.0713
Epoch 2/2... Discriminator Loss: 1.1046... Generator Loss: 0.6917
Epoch 2/2... Discriminator Loss: 1.0957... Generator Loss: 1.0126
Epoch 2/2... Discriminator Loss: 1.2835... Generator Loss: 0.4792
Epoch 2/2... Discriminator Loss: 1.0033... Generator Loss: 0.8834
Epoch 2/2... Discriminator Loss: 0.9893... Generator Loss: 0.8548
Epoch 2/2... Discriminator Loss: 1.3137... Generator Loss: 0.4669
Epoch 2/2... Discriminator Loss: 1.1091... Generator Loss: 0.6673
Epoch 2/2... Discriminator Loss: 1.0288... Generator Loss: 0.7694
Epoch 2/2... Discriminator Loss: 3.0950... Generator Loss: 0.0490
Epoch 2/2... Discriminator Loss: 1.1657... Generator Loss: 0.8589
Epoch 2/2... Discriminator Loss: 1.0987... Generator Loss: 0.6738
Epoch 2/2... Discriminator Loss: 0.9892... Generator Loss: 0.9055
Epoch 2/2... Discriminator Loss: 1.0090... Generator Loss: 0.8642
Epoch 2/2... Discriminator Loss: 1.0729... Generator Loss: 0.9020
Epoch 2/2... Discriminator Loss: 1.0248... Generator Loss: 0.8848
Epoch 2/2... Discriminator Loss: 0.9672... Generator Loss: 0.7599
Epoch 2/2... Discriminator Loss: 1.0612... Generator Loss: 0.6641
Epoch 2/2... Discriminator Loss: 0.8945... Generator Loss: 1.3477
Epoch 2/2... Discriminator Loss: 0.9515... Generator Loss: 0.8406
Epoch 2/2... Discriminator Loss: 0.9641... Generator Loss: 1.4078
Epoch 2/2... Discriminator Loss: 1.0448... Generator Loss: 0.6610
Epoch 2/2... Discriminator Loss: 1.0561... Generator Loss: 0.9021
Epoch 2/2... Discriminator Loss: 0.9322... Generator Loss: 0.8590
Epoch 2/2... Discriminator Loss: 0.9410... Generator Loss: 1.0585
Epoch 2/2... Discriminator Loss: 0.8771... Generator Loss: 1.0188
Epoch 2/2... Discriminator Loss: 3.0540... Generator Loss: 0.0473
Epoch 2/2... Discriminator Loss: 0.8955... Generator Loss: 0.8323
Epoch 2/2... Discriminator Loss: 0.8907... Generator Loss: 0.9747
Epoch 2/2... Discriminator Loss: 1.0505... Generator Loss: 0.7135
Epoch 2/2... Discriminator Loss: 1.0414... Generator Loss: 0.9255
Epoch 2/2... Discriminator Loss: 0.9439... Generator Loss: 1.1709
Epoch 2/2... Discriminator Loss: 1.0485... Generator Loss: 0.8402
Epoch 2/2... Discriminator Loss: 0.9442... Generator Loss: 1.0557
Epoch 2/2... Discriminator Loss: 1.1926... Generator Loss: 0.5366
Epoch 2/2... Discriminator Loss: 0.9042... Generator Loss: 1.0707
Epoch 2/2... Discriminator Loss: 0.9557... Generator Loss: 1.7065
Epoch 2/2... Discriminator Loss: 1.0311... Generator Loss: 0.6791
Epoch 2/2... Discriminator Loss: 1.0947... Generator Loss: 0.7761
Epoch 2/2... Discriminator Loss: 0.8972... Generator Loss: 1.0693
Epoch 2/2... Discriminator Loss: 1.2080... Generator Loss: 0.4606
Epoch 2/2... Discriminator Loss: 1.4040... Generator Loss: 0.4400
Epoch 2/2... Discriminator Loss: 1.8969... Generator Loss: 1.6080
Epoch 2/2... Discriminator Loss: 1.1844... Generator Loss: 1.0913
Epoch 2/2... Discriminator Loss: 1.1747... Generator Loss: 0.8220
Epoch 2/2... Discriminator Loss: 1.1373... Generator Loss: 0.6341
Epoch 2/2... Discriminator Loss: 1.0062... Generator Loss: 0.8539
Epoch 2/2... Discriminator Loss: 0.8941... Generator Loss: 1.1626
Epoch 2/2... Discriminator Loss: 0.9737... Generator Loss: 0.9504
Epoch 2/2... Discriminator Loss: 0.9448... Generator Loss: 1.0589
Epoch 2/2... Discriminator Loss: 0.9196... Generator Loss: 0.9004
Epoch 2/2... Discriminator Loss: 0.8827... Generator Loss: 1.0208
Epoch 2/2... Discriminator Loss: 0.9945... Generator Loss: 1.2196
Epoch 2/2... Discriminator Loss: 0.8882... Generator Loss: 1.2369
Epoch 2/2... Discriminator Loss: 0.8932... Generator Loss: 1.0411
Epoch 2/2... Discriminator Loss: 0.9682... Generator Loss: 0.8960
Epoch 2/2... Discriminator Loss: 0.8487... Generator Loss: 0.9730
Epoch 2/2... Discriminator Loss: 0.8479... Generator Loss: 1.1336
Epoch 2/2... Discriminator Loss: 1.2001... Generator Loss: 0.5885
Epoch 2/2... Discriminator Loss: 0.9084... Generator Loss: 1.1272
Epoch 2/2... Discriminator Loss: 0.8127... Generator Loss: 0.9725
Epoch 2/2... Discriminator Loss: 0.8988... Generator Loss: 0.7118
Epoch 2/2... Discriminator Loss: 0.8887... Generator Loss: 1.4353
Epoch 2/2... Discriminator Loss: 0.9231... Generator Loss: 0.8909
Epoch 2/2... Discriminator Loss: 1.0679... Generator Loss: 1.1432
Epoch 2/2... Discriminator Loss: 1.1722... Generator Loss: 1.0394
Epoch 2/2... Discriminator Loss: 1.2131... Generator Loss: 0.7105
Epoch 2/2... Discriminator Loss: 0.9858... Generator Loss: 1.0695
Epoch 2/2... Discriminator Loss: 0.9603... Generator Loss: 0.8565
Epoch 2/2... Discriminator Loss: 0.9876... Generator Loss: 0.9007
Epoch 2/2... Discriminator Loss: 0.8995... Generator Loss: 1.2221
Epoch 2/2... Discriminator Loss: 0.9156... Generator Loss: 0.9649
Epoch 2/2... Discriminator Loss: 0.9316... Generator Loss: 0.9246
Epoch 2/2... Discriminator Loss: 0.8180... Generator Loss: 1.2689
Epoch 2/2... Discriminator Loss: 0.7433... Generator Loss: 1.2872
Epoch 2/2... Discriminator Loss: 0.8095... Generator Loss: 1.2412
Epoch 2/2... Discriminator Loss: 0.8892... Generator Loss: 1.3875
Epoch 2/2... Discriminator Loss: 0.8716... Generator Loss: 0.9307
Epoch 2/2... Discriminator Loss: 0.7412... Generator Loss: 1.0857
Epoch 2/2... Discriminator Loss: 0.8189... Generator Loss: 1.2147
Epoch 2/2... Discriminator Loss: 0.9111... Generator Loss: 1.3098
Epoch 2/2... Discriminator Loss: 0.9042... Generator Loss: 1.2906
Epoch 2/2... Discriminator Loss: 1.4978... Generator Loss: 2.9414
Epoch 2/2... Discriminator Loss: 0.8417... Generator Loss: 1.2908
Epoch 2/2... Discriminator Loss: 1.0713... Generator Loss: 0.7541
Epoch 2/2... Discriminator Loss: 0.8124... Generator Loss: 1.2199
Epoch 2/2... Discriminator Loss: 0.8466... Generator Loss: 1.0404
Epoch 2/2... Discriminator Loss: 0.8831... Generator Loss: 0.9143
Epoch 2/2... Discriminator Loss: 1.0268... Generator Loss: 0.6248
Epoch 2/2... Discriminator Loss: 0.8656... Generator Loss: 1.1875
Epoch 2/2... Discriminator Loss: 0.9701... Generator Loss: 0.8478
Epoch 2/2... Discriminator Loss: 0.9591... Generator Loss: 0.8913
Epoch 2/2... Discriminator Loss: 0.9716... Generator Loss: 0.7873
Epoch 2/2... Discriminator Loss: 0.8946... Generator Loss: 1.6148
Epoch 2/2... Discriminator Loss: 0.8169... Generator Loss: 1.3303

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [16]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
tf.reset_default_graph()

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.0202... Generator Loss: 1.1558
Epoch 1/1... Discriminator Loss: 0.9778... Generator Loss: 0.7002
Epoch 1/1... Discriminator Loss: 1.0340... Generator Loss: 0.7037
Epoch 1/1... Discriminator Loss: 1.0042... Generator Loss: 1.5187
Epoch 1/1... Discriminator Loss: 2.3447... Generator Loss: 0.1739
Epoch 1/1... Discriminator Loss: 1.0384... Generator Loss: 2.4741
Epoch 1/1... Discriminator Loss: 2.1601... Generator Loss: 0.2214
Epoch 1/1... Discriminator Loss: 1.3886... Generator Loss: 0.6768
Epoch 1/1... Discriminator Loss: 1.0982... Generator Loss: 0.9572
Epoch 1/1... Discriminator Loss: 1.0481... Generator Loss: 0.9522
Epoch 1/1... Discriminator Loss: 0.9742... Generator Loss: 0.9582
Epoch 1/1... Discriminator Loss: 1.1661... Generator Loss: 1.0590
Epoch 1/1... Discriminator Loss: 0.9651... Generator Loss: 1.1782
Epoch 1/1... Discriminator Loss: 1.1658... Generator Loss: 1.5918
Epoch 1/1... Discriminator Loss: 1.0974... Generator Loss: 0.6662
Epoch 1/1... Discriminator Loss: 1.3159... Generator Loss: 0.8248
Epoch 1/1... Discriminator Loss: 1.1138... Generator Loss: 0.6628
Epoch 1/1... Discriminator Loss: 1.0206... Generator Loss: 1.6743
Epoch 1/1... Discriminator Loss: 0.9505... Generator Loss: 1.6200
Epoch 1/1... Discriminator Loss: 1.1375... Generator Loss: 0.6896
Epoch 1/1... Discriminator Loss: 1.0471... Generator Loss: 0.7615
Epoch 1/1... Discriminator Loss: 1.1330... Generator Loss: 0.7355
Epoch 1/1... Discriminator Loss: 0.9984... Generator Loss: 1.7252
Epoch 1/1... Discriminator Loss: 1.5291... Generator Loss: 0.4287
Epoch 1/1... Discriminator Loss: 1.1349... Generator Loss: 1.6962
Epoch 1/1... Discriminator Loss: 1.0989... Generator Loss: 1.1991
Epoch 1/1... Discriminator Loss: 1.1296... Generator Loss: 0.6419
Epoch 1/1... Discriminator Loss: 1.0002... Generator Loss: 0.9738
Epoch 1/1... Discriminator Loss: 0.9866... Generator Loss: 1.2523
Epoch 1/1... Discriminator Loss: 0.9979... Generator Loss: 2.4154
Epoch 1/1... Discriminator Loss: 0.9074... Generator Loss: 1.6089
Epoch 1/1... Discriminator Loss: 1.1471... Generator Loss: 1.5552
Epoch 1/1... Discriminator Loss: 1.0004... Generator Loss: 0.7134
Epoch 1/1... Discriminator Loss: 1.2664... Generator Loss: 2.8606
Epoch 1/1... Discriminator Loss: 0.9393... Generator Loss: 1.1904
Epoch 1/1... Discriminator Loss: 0.9843... Generator Loss: 1.5069
Epoch 1/1... Discriminator Loss: 1.1106... Generator Loss: 0.6704
Epoch 1/1... Discriminator Loss: 1.0025... Generator Loss: 0.7382
Epoch 1/1... Discriminator Loss: 1.0001... Generator Loss: 1.9771
Epoch 1/1... Discriminator Loss: 1.0213... Generator Loss: 1.2184
Epoch 1/1... Discriminator Loss: 1.0556... Generator Loss: 1.4921
Epoch 1/1... Discriminator Loss: 1.1716... Generator Loss: 1.3657
Epoch 1/1... Discriminator Loss: 1.0399... Generator Loss: 1.3537
Epoch 1/1... Discriminator Loss: 1.0079... Generator Loss: 1.1416
Epoch 1/1... Discriminator Loss: 0.9599... Generator Loss: 1.0134
Epoch 1/1... Discriminator Loss: 1.3299... Generator Loss: 0.5279
Epoch 1/1... Discriminator Loss: 1.3647... Generator Loss: 0.5338
Epoch 1/1... Discriminator Loss: 0.9951... Generator Loss: 0.9584
Epoch 1/1... Discriminator Loss: 0.9430... Generator Loss: 1.2518
Epoch 1/1... Discriminator Loss: 1.0474... Generator Loss: 1.5125
Epoch 1/1... Discriminator Loss: 0.9798... Generator Loss: 1.0470
Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 0.5497
Epoch 1/1... Discriminator Loss: 0.9554... Generator Loss: 1.7908
Epoch 1/1... Discriminator Loss: 1.1278... Generator Loss: 0.8350
Epoch 1/1... Discriminator Loss: 1.5562... Generator Loss: 0.3315
Epoch 1/1... Discriminator Loss: 1.1737... Generator Loss: 0.6275
Epoch 1/1... Discriminator Loss: 1.1184... Generator Loss: 0.7534
Epoch 1/1... Discriminator Loss: 1.4706... Generator Loss: 1.8281
Epoch 1/1... Discriminator Loss: 1.1894... Generator Loss: 0.6163
Epoch 1/1... Discriminator Loss: 1.0637... Generator Loss: 1.0210
Epoch 1/1... Discriminator Loss: 1.0549... Generator Loss: 1.3055
Epoch 1/1... Discriminator Loss: 1.4140... Generator Loss: 0.4433
Epoch 1/1... Discriminator Loss: 1.0302... Generator Loss: 1.4855
Epoch 1/1... Discriminator Loss: 1.4156... Generator Loss: 0.5181
Epoch 1/1... Discriminator Loss: 1.2309... Generator Loss: 0.8548
Epoch 1/1... Discriminator Loss: 1.2281... Generator Loss: 0.8257
Epoch 1/1... Discriminator Loss: 1.0091... Generator Loss: 0.9173
Epoch 1/1... Discriminator Loss: 1.1757... Generator Loss: 1.1568
Epoch 1/1... Discriminator Loss: 1.1975... Generator Loss: 1.4701
Epoch 1/1... Discriminator Loss: 1.2163... Generator Loss: 0.6425
Epoch 1/1... Discriminator Loss: 1.1569... Generator Loss: 1.0560
Epoch 1/1... Discriminator Loss: 1.2233... Generator Loss: 0.6976
Epoch 1/1... Discriminator Loss: 1.1635... Generator Loss: 0.7273
Epoch 1/1... Discriminator Loss: 1.2298... Generator Loss: 0.7652
Epoch 1/1... Discriminator Loss: 1.2242... Generator Loss: 0.6759
Epoch 1/1... Discriminator Loss: 1.4187... Generator Loss: 0.4187
Epoch 1/1... Discriminator Loss: 1.1236... Generator Loss: 0.8398
Epoch 1/1... Discriminator Loss: 1.1751... Generator Loss: 0.6760
Epoch 1/1... Discriminator Loss: 1.2775... Generator Loss: 0.5699
Epoch 1/1... Discriminator Loss: 1.1927... Generator Loss: 1.1298
Epoch 1/1... Discriminator Loss: 1.0844... Generator Loss: 1.1569
Epoch 1/1... Discriminator Loss: 1.1883... Generator Loss: 0.7432
Epoch 1/1... Discriminator Loss: 1.3383... Generator Loss: 0.3682
Epoch 1/1... Discriminator Loss: 1.0925... Generator Loss: 0.6905
Epoch 1/1... Discriminator Loss: 1.1099... Generator Loss: 0.8701
Epoch 1/1... Discriminator Loss: 1.4531... Generator Loss: 0.4955
Epoch 1/1... Discriminator Loss: 1.2731... Generator Loss: 0.4945
Epoch 1/1... Discriminator Loss: 1.3001... Generator Loss: 0.7917
Epoch 1/1... Discriminator Loss: 1.1905... Generator Loss: 1.0656
Epoch 1/1... Discriminator Loss: 1.0865... Generator Loss: 0.7557
Epoch 1/1... Discriminator Loss: 1.4580... Generator Loss: 0.3890
Epoch 1/1... Discriminator Loss: 1.2934... Generator Loss: 0.6287
Epoch 1/1... Discriminator Loss: 1.3723... Generator Loss: 0.8134
Epoch 1/1... Discriminator Loss: 1.1346... Generator Loss: 0.6877
Epoch 1/1... Discriminator Loss: 1.1037... Generator Loss: 1.0075
Epoch 1/1... Discriminator Loss: 1.3534... Generator Loss: 0.5563
Epoch 1/1... Discriminator Loss: 1.1885... Generator Loss: 0.8670
Epoch 1/1... Discriminator Loss: 1.2017... Generator Loss: 0.7483
Epoch 1/1... Discriminator Loss: 1.4823... Generator Loss: 0.5360
Epoch 1/1... Discriminator Loss: 1.0725... Generator Loss: 1.1456
Epoch 1/1... Discriminator Loss: 1.1634... Generator Loss: 0.9341
Epoch 1/1... Discriminator Loss: 1.1283... Generator Loss: 0.8288
Epoch 1/1... Discriminator Loss: 1.1495... Generator Loss: 1.1585
Epoch 1/1... Discriminator Loss: 1.1747... Generator Loss: 1.1601
Epoch 1/1... Discriminator Loss: 1.1562... Generator Loss: 0.9169
Epoch 1/1... Discriminator Loss: 1.2629... Generator Loss: 0.6458
Epoch 1/1... Discriminator Loss: 1.2905... Generator Loss: 0.7315
Epoch 1/1... Discriminator Loss: 1.1291... Generator Loss: 1.1455
Epoch 1/1... Discriminator Loss: 1.2808... Generator Loss: 0.8573
Epoch 1/1... Discriminator Loss: 1.0711... Generator Loss: 0.8766
Epoch 1/1... Discriminator Loss: 1.1959... Generator Loss: 0.8268
Epoch 1/1... Discriminator Loss: 1.3301... Generator Loss: 0.5854
Epoch 1/1... Discriminator Loss: 1.0491... Generator Loss: 0.8647
Epoch 1/1... Discriminator Loss: 0.9364... Generator Loss: 1.1750
Epoch 1/1... Discriminator Loss: 1.0746... Generator Loss: 1.3586
Epoch 1/1... Discriminator Loss: 1.1923... Generator Loss: 0.6877
Epoch 1/1... Discriminator Loss: 1.3955... Generator Loss: 1.4373
Epoch 1/1... Discriminator Loss: 1.1750... Generator Loss: 0.9362
Epoch 1/1... Discriminator Loss: 1.2440... Generator Loss: 0.9847
Epoch 1/1... Discriminator Loss: 1.1031... Generator Loss: 0.8563
Epoch 1/1... Discriminator Loss: 1.1529... Generator Loss: 0.8780
Epoch 1/1... Discriminator Loss: 1.1077... Generator Loss: 0.9481
Epoch 1/1... Discriminator Loss: 1.0776... Generator Loss: 0.8211
Epoch 1/1... Discriminator Loss: 1.1336... Generator Loss: 0.7964
Epoch 1/1... Discriminator Loss: 1.1638... Generator Loss: 0.6341
Epoch 1/1... Discriminator Loss: 1.1854... Generator Loss: 0.6417
Epoch 1/1... Discriminator Loss: 1.1724... Generator Loss: 0.9256
Epoch 1/1... Discriminator Loss: 1.0139... Generator Loss: 1.1231
Epoch 1/1... Discriminator Loss: 1.3411... Generator Loss: 0.4318
Epoch 1/1... Discriminator Loss: 1.2083... Generator Loss: 0.7256
Epoch 1/1... Discriminator Loss: 1.2093... Generator Loss: 0.6808
Epoch 1/1... Discriminator Loss: 1.0949... Generator Loss: 0.7745
Epoch 1/1... Discriminator Loss: 1.2283... Generator Loss: 0.8187
Epoch 1/1... Discriminator Loss: 1.2847... Generator Loss: 0.6252
Epoch 1/1... Discriminator Loss: 1.0876... Generator Loss: 0.7499
Epoch 1/1... Discriminator Loss: 1.1382... Generator Loss: 1.0471
Epoch 1/1... Discriminator Loss: 1.2576... Generator Loss: 0.8403
Epoch 1/1... Discriminator Loss: 1.1611... Generator Loss: 0.6605
Epoch 1/1... Discriminator Loss: 1.1907... Generator Loss: 1.0025
Epoch 1/1... Discriminator Loss: 1.0565... Generator Loss: 1.1580
Epoch 1/1... Discriminator Loss: 1.1722... Generator Loss: 0.8974
Epoch 1/1... Discriminator Loss: 1.0804... Generator Loss: 0.9115
Epoch 1/1... Discriminator Loss: 1.2726... Generator Loss: 0.6541
Epoch 1/1... Discriminator Loss: 1.0553... Generator Loss: 0.9575
Epoch 1/1... Discriminator Loss: 1.3338... Generator Loss: 0.7365
Epoch 1/1... Discriminator Loss: 1.2561... Generator Loss: 0.7416
Epoch 1/1... Discriminator Loss: 1.2119... Generator Loss: 0.9646
Epoch 1/1... Discriminator Loss: 1.1892... Generator Loss: 0.8843
Epoch 1/1... Discriminator Loss: 1.0994... Generator Loss: 0.9455
Epoch 1/1... Discriminator Loss: 1.0889... Generator Loss: 0.7676
Epoch 1/1... Discriminator Loss: 1.2105... Generator Loss: 1.4684
Epoch 1/1... Discriminator Loss: 1.1658... Generator Loss: 0.6874
Epoch 1/1... Discriminator Loss: 1.2999... Generator Loss: 0.8581
Epoch 1/1... Discriminator Loss: 1.3798... Generator Loss: 0.3798
Epoch 1/1... Discriminator Loss: 1.1945... Generator Loss: 0.8278
Epoch 1/1... Discriminator Loss: 1.1976... Generator Loss: 0.6841
Epoch 1/1... Discriminator Loss: 1.0992... Generator Loss: 0.7600
Epoch 1/1... Discriminator Loss: 1.0920... Generator Loss: 0.6959
Epoch 1/1... Discriminator Loss: 1.3349... Generator Loss: 1.3565
Epoch 1/1... Discriminator Loss: 1.1263... Generator Loss: 0.8126
Epoch 1/1... Discriminator Loss: 1.1308... Generator Loss: 0.9589
Epoch 1/1... Discriminator Loss: 1.1206... Generator Loss: 0.9784
Epoch 1/1... Discriminator Loss: 1.0816... Generator Loss: 0.7814
Epoch 1/1... Discriminator Loss: 1.1495... Generator Loss: 0.8294
Epoch 1/1... Discriminator Loss: 1.4101... Generator Loss: 0.4096
Epoch 1/1... Discriminator Loss: 1.1004... Generator Loss: 0.9682
Epoch 1/1... Discriminator Loss: 1.0373... Generator Loss: 1.0878
Epoch 1/1... Discriminator Loss: 1.2702... Generator Loss: 0.7909
Epoch 1/1... Discriminator Loss: 1.2424... Generator Loss: 0.5840
Epoch 1/1... Discriminator Loss: 1.2735... Generator Loss: 0.5255
Epoch 1/1... Discriminator Loss: 1.1673... Generator Loss: 0.8267
Epoch 1/1... Discriminator Loss: 1.1290... Generator Loss: 0.8377
Epoch 1/1... Discriminator Loss: 1.2874... Generator Loss: 0.5474
Epoch 1/1... Discriminator Loss: 1.2204... Generator Loss: 1.0305
Epoch 1/1... Discriminator Loss: 1.3864... Generator Loss: 0.4365
Epoch 1/1... Discriminator Loss: 1.2256... Generator Loss: 0.7628
Epoch 1/1... Discriminator Loss: 1.2439... Generator Loss: 0.4962
Epoch 1/1... Discriminator Loss: 1.3060... Generator Loss: 0.9771
Epoch 1/1... Discriminator Loss: 1.3275... Generator Loss: 0.9911
Epoch 1/1... Discriminator Loss: 1.2390... Generator Loss: 0.5352
Epoch 1/1... Discriminator Loss: 1.2457... Generator Loss: 0.9816
Epoch 1/1... Discriminator Loss: 1.0860... Generator Loss: 1.0046
Epoch 1/1... Discriminator Loss: 1.1837... Generator Loss: 0.6821
Epoch 1/1... Discriminator Loss: 1.2612... Generator Loss: 0.5538
Epoch 1/1... Discriminator Loss: 0.9887... Generator Loss: 1.0254
Epoch 1/1... Discriminator Loss: 1.3932... Generator Loss: 0.6205
Epoch 1/1... Discriminator Loss: 1.2252... Generator Loss: 1.6302
Epoch 1/1... Discriminator Loss: 1.2687... Generator Loss: 0.6488
Epoch 1/1... Discriminator Loss: 1.4629... Generator Loss: 0.4829
Epoch 1/1... Discriminator Loss: 1.1816... Generator Loss: 0.8486
Epoch 1/1... Discriminator Loss: 1.2678... Generator Loss: 0.8156
Epoch 1/1... Discriminator Loss: 1.2070... Generator Loss: 0.7949
Epoch 1/1... Discriminator Loss: 1.1220... Generator Loss: 0.8806
Epoch 1/1... Discriminator Loss: 1.2365... Generator Loss: 0.8388
Epoch 1/1... Discriminator Loss: 1.2141... Generator Loss: 1.1479
Epoch 1/1... Discriminator Loss: 1.2137... Generator Loss: 0.7118
Epoch 1/1... Discriminator Loss: 1.2264... Generator Loss: 0.6817
Epoch 1/1... Discriminator Loss: 1.1189... Generator Loss: 0.7757
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.4962
Epoch 1/1... Discriminator Loss: 1.2147... Generator Loss: 0.5971
Epoch 1/1... Discriminator Loss: 1.1156... Generator Loss: 0.7807
Epoch 1/1... Discriminator Loss: 1.2686... Generator Loss: 0.6411
Epoch 1/1... Discriminator Loss: 1.1851... Generator Loss: 0.8380
Epoch 1/1... Discriminator Loss: 1.1083... Generator Loss: 1.0608
Epoch 1/1... Discriminator Loss: 1.1127... Generator Loss: 0.9865
Epoch 1/1... Discriminator Loss: 1.1382... Generator Loss: 0.6640
Epoch 1/1... Discriminator Loss: 1.0717... Generator Loss: 0.8405
Epoch 1/1... Discriminator Loss: 1.1613... Generator Loss: 0.9537
Epoch 1/1... Discriminator Loss: 1.2312... Generator Loss: 0.7771
Epoch 1/1... Discriminator Loss: 1.0774... Generator Loss: 0.8978
Epoch 1/1... Discriminator Loss: 1.2593... Generator Loss: 0.5605
Epoch 1/1... Discriminator Loss: 1.2166... Generator Loss: 0.6744
Epoch 1/1... Discriminator Loss: 0.9242... Generator Loss: 1.1046
Epoch 1/1... Discriminator Loss: 1.2509... Generator Loss: 0.7261
Epoch 1/1... Discriminator Loss: 1.2706... Generator Loss: 0.5634
Epoch 1/1... Discriminator Loss: 1.2842... Generator Loss: 0.8183
Epoch 1/1... Discriminator Loss: 1.2686... Generator Loss: 0.8273
Epoch 1/1... Discriminator Loss: 1.2210... Generator Loss: 0.7131
Epoch 1/1... Discriminator Loss: 1.1228... Generator Loss: 0.9440
Epoch 1/1... Discriminator Loss: 1.0949... Generator Loss: 1.2270
Epoch 1/1... Discriminator Loss: 1.2480... Generator Loss: 0.6614
Epoch 1/1... Discriminator Loss: 0.9544... Generator Loss: 0.9531
Epoch 1/1... Discriminator Loss: 1.4253... Generator Loss: 0.4737
Epoch 1/1... Discriminator Loss: 1.0573... Generator Loss: 0.7042
Epoch 1/1... Discriminator Loss: 1.1507... Generator Loss: 0.7303
Epoch 1/1... Discriminator Loss: 1.4075... Generator Loss: 0.4776
Epoch 1/1... Discriminator Loss: 1.0566... Generator Loss: 0.7307
Epoch 1/1... Discriminator Loss: 1.1849... Generator Loss: 0.7468
Epoch 1/1... Discriminator Loss: 1.1689... Generator Loss: 0.9014
Epoch 1/1... Discriminator Loss: 1.3399... Generator Loss: 0.5124
Epoch 1/1... Discriminator Loss: 1.2619... Generator Loss: 0.5049
Epoch 1/1... Discriminator Loss: 1.2566... Generator Loss: 0.6180
Epoch 1/1... Discriminator Loss: 1.1866... Generator Loss: 0.7153
Epoch 1/1... Discriminator Loss: 1.0320... Generator Loss: 0.7814
Epoch 1/1... Discriminator Loss: 1.1151... Generator Loss: 0.9418
Epoch 1/1... Discriminator Loss: 1.2480... Generator Loss: 0.6654
Epoch 1/1... Discriminator Loss: 1.2165... Generator Loss: 0.7675
Epoch 1/1... Discriminator Loss: 1.0608... Generator Loss: 0.9118
Epoch 1/1... Discriminator Loss: 1.2874... Generator Loss: 0.9610
Epoch 1/1... Discriminator Loss: 1.3627... Generator Loss: 0.5100
Epoch 1/1... Discriminator Loss: 1.4413... Generator Loss: 0.4228
Epoch 1/1... Discriminator Loss: 1.0889... Generator Loss: 0.8450
Epoch 1/1... Discriminator Loss: 1.2377... Generator Loss: 0.9038
Epoch 1/1... Discriminator Loss: 1.1864... Generator Loss: 0.6538
Epoch 1/1... Discriminator Loss: 1.1942... Generator Loss: 0.8424
Epoch 1/1... Discriminator Loss: 1.2473... Generator Loss: 0.5266
Epoch 1/1... Discriminator Loss: 1.2388... Generator Loss: 0.6732
Epoch 1/1... Discriminator Loss: 1.2239... Generator Loss: 0.6007
Epoch 1/1... Discriminator Loss: 1.1252... Generator Loss: 0.6502
Epoch 1/1... Discriminator Loss: 1.1257... Generator Loss: 0.8496
Epoch 1/1... Discriminator Loss: 1.0947... Generator Loss: 0.5765
Epoch 1/1... Discriminator Loss: 1.2237... Generator Loss: 0.9336
Epoch 1/1... Discriminator Loss: 1.2461... Generator Loss: 0.7527
Epoch 1/1... Discriminator Loss: 1.4796... Generator Loss: 0.4041
Epoch 1/1... Discriminator Loss: 1.0786... Generator Loss: 0.8677
Epoch 1/1... Discriminator Loss: 1.1992... Generator Loss: 0.6494
Epoch 1/1... Discriminator Loss: 1.2740... Generator Loss: 0.5653
Epoch 1/1... Discriminator Loss: 0.9727... Generator Loss: 1.0368
Epoch 1/1... Discriminator Loss: 1.2354... Generator Loss: 0.7032
Epoch 1/1... Discriminator Loss: 1.2246... Generator Loss: 0.7322
Epoch 1/1... Discriminator Loss: 1.2182... Generator Loss: 0.7905
Epoch 1/1... Discriminator Loss: 1.1235... Generator Loss: 0.8343
Epoch 1/1... Discriminator Loss: 1.1825... Generator Loss: 0.5861
Epoch 1/1... Discriminator Loss: 1.2192... Generator Loss: 0.6985
Epoch 1/1... Discriminator Loss: 1.1769... Generator Loss: 0.7928
Epoch 1/1... Discriminator Loss: 1.2235... Generator Loss: 0.8289
Epoch 1/1... Discriminator Loss: 1.1420... Generator Loss: 0.8289
Epoch 1/1... Discriminator Loss: 1.1847... Generator Loss: 0.7493
Epoch 1/1... Discriminator Loss: 1.1215... Generator Loss: 0.7317
Epoch 1/1... Discriminator Loss: 1.3337... Generator Loss: 0.3960
Epoch 1/1... Discriminator Loss: 1.0668... Generator Loss: 0.7722
Epoch 1/1... Discriminator Loss: 1.0067... Generator Loss: 1.1286
Epoch 1/1... Discriminator Loss: 1.2090... Generator Loss: 0.6336
Epoch 1/1... Discriminator Loss: 1.1342... Generator Loss: 0.7441
Epoch 1/1... Discriminator Loss: 1.2460... Generator Loss: 1.0729
Epoch 1/1... Discriminator Loss: 1.4287... Generator Loss: 0.3669
Epoch 1/1... Discriminator Loss: 1.2695... Generator Loss: 0.8806
Epoch 1/1... Discriminator Loss: 1.0405... Generator Loss: 0.8799
Epoch 1/1... Discriminator Loss: 1.1109... Generator Loss: 0.9365
Epoch 1/1... Discriminator Loss: 1.2141... Generator Loss: 0.7145
Epoch 1/1... Discriminator Loss: 1.1266... Generator Loss: 0.7569
Epoch 1/1... Discriminator Loss: 1.1811... Generator Loss: 1.0314
Epoch 1/1... Discriminator Loss: 1.1030... Generator Loss: 0.6920
Epoch 1/1... Discriminator Loss: 1.2456... Generator Loss: 0.6647
Epoch 1/1... Discriminator Loss: 1.2215... Generator Loss: 0.6415
Epoch 1/1... Discriminator Loss: 1.2066... Generator Loss: 0.7657
Epoch 1/1... Discriminator Loss: 1.2748... Generator Loss: 0.6320
Epoch 1/1... Discriminator Loss: 0.9449... Generator Loss: 1.1153
Epoch 1/1... Discriminator Loss: 1.2543... Generator Loss: 0.9230
Epoch 1/1... Discriminator Loss: 1.1839... Generator Loss: 0.8673
Epoch 1/1... Discriminator Loss: 1.1143... Generator Loss: 0.7013
Epoch 1/1... Discriminator Loss: 1.1769... Generator Loss: 1.1452
Epoch 1/1... Discriminator Loss: 1.1361... Generator Loss: 0.7154
Epoch 1/1... Discriminator Loss: 1.2920... Generator Loss: 0.5761
Epoch 1/1... Discriminator Loss: 1.0653... Generator Loss: 1.3538
Epoch 1/1... Discriminator Loss: 1.1988... Generator Loss: 0.6982
Epoch 1/1... Discriminator Loss: 0.9935... Generator Loss: 1.0815
Epoch 1/1... Discriminator Loss: 1.3093... Generator Loss: 0.5787
Epoch 1/1... Discriminator Loss: 1.0506... Generator Loss: 1.0221
Epoch 1/1... Discriminator Loss: 1.0831... Generator Loss: 0.7791
Epoch 1/1... Discriminator Loss: 1.0980... Generator Loss: 0.6526
Epoch 1/1... Discriminator Loss: 1.0563... Generator Loss: 1.4581
Epoch 1/1... Discriminator Loss: 1.3532... Generator Loss: 0.3485
Epoch 1/1... Discriminator Loss: 1.1306... Generator Loss: 0.8907
Epoch 1/1... Discriminator Loss: 1.0698... Generator Loss: 0.9101
Epoch 1/1... Discriminator Loss: 1.2416... Generator Loss: 0.6460
Epoch 1/1... Discriminator Loss: 1.0178... Generator Loss: 1.0006
Epoch 1/1... Discriminator Loss: 1.0063... Generator Loss: 1.2309
Epoch 1/1... Discriminator Loss: 1.1434... Generator Loss: 0.9294
Epoch 1/1... Discriminator Loss: 1.3061... Generator Loss: 0.8120
Epoch 1/1... Discriminator Loss: 1.1353... Generator Loss: 0.9334
Epoch 1/1... Discriminator Loss: 1.1607... Generator Loss: 0.7032
Epoch 1/1... Discriminator Loss: 1.1177... Generator Loss: 1.1122
Epoch 1/1... Discriminator Loss: 1.3031... Generator Loss: 0.4906
Epoch 1/1... Discriminator Loss: 1.1070... Generator Loss: 1.2172
Epoch 1/1... Discriminator Loss: 0.9646... Generator Loss: 0.9211

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.